Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Intelligent Hybrid GA-PI Feature Selection Technique for Network Intrusion Detection Systems Subject The topic of the resource Feature selection; Hybrid GA-PI algorithm; Network Intrusion Detection Systems (NIDS); Support vector machines (SVM) Description An account of the resource The development of Network Intrusion Detection Systems (NIDS) has become increasingly important due to the growing threat of cyber-attacks. However, with the vast amount of data generated in networks, handling big data in NIDS has become a major challenge. To address this challenge, this research paper proposes an intelligent hybrid GA-PI algorithm for feature selection and classification tasks in NIDS using support vector machines (SVM). The proposed approach is evaluated using two sub-datasets, Analysis and Normal, and Reconnaissance and Normal, which are generated from the publicly available UNSWNB-15 dataset. In this work, instead of considering all possible attacks, the focus is on two attacks, emphasizing the importance of the feature selection agent in determining the optimal features based on the attack type. The experimental results show that the proposed hybrid feature selection approach outperforms existing methodologies in terms of accuracy and execution time. Moreover, the selection of features can be subjective and dependent on the domain knowledge of the researcher. Additionally, the proposed approach requires computational resources for feature selection and classification tasks, which can be a limitation for resource-constrained systems. To be brief, this research paper presents a promising approach for feature selection and classification tasks in NIDS using an intelligent hybrid GA-PI algorithm. While there are some challenges and limitations, the proposed approach has the potential to contribute to the development of effective and efficient NIDS. 2023, Ismail Saritas. All rights reserved. Creator An entity primarily responsible for making the resource Sowmya; Mary Anita T. Source A related resource from which the described resource is derived International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 7s, pp. 718-731. Publisher An entity responsible for making the resource available Ismail Saritas Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165153935&amp;partnerID=40&amp;md5=61ac7ed13d9a5cb1b861cf350fb54da6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165153935&amp;partnerID=40&amp;md5=61ac7ed13d9a5cb1b861cf350fb54da6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21476799 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sowmya, Christ University &amp; Assistant professor, CMRIT, Karnatka, Bangalore, India; Mary Anita T., Christ University Bangalore, Karnatka, Bangalore, India